ArticleRadiology. Cardiothoracic imaging2023
Artificial Intelligence-based Coronary Stenosis Quantification at Coronary CT Angiography versus Quantitative Coronary Angiography.
Article in Radiology. Cardiothoracic imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 22 citations in OpenAlex.
- Advanced quantitative CT for coronary artery disease: integrating stenosis quantification, plaque quantification, and fractional flow reserve-CT.The British journal of radiology · 2026Review
- Artificial intelligence detection of heart failure on coronary computed tomography angiography: external validation in patients with non-ST-segment elevation acute coronary syndrome.European heart journal. Digital health · 2026Article
- Human-AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
- Clinical Applications of Artificial Intelligence in Cardiac CT: From Coronary CT Angiography to CT-Derived Fractional Flow Reserve and Myocardial Perfusion Imaging.Korean journal of radiology · 2026Review
- Utility of artificial intelligence in predicting clinical outcomes of angiographic procedures: a comprehensive scoping review.Journal of cardiovascular imaging · 2026Review
- Coronary stenosis assessment: AI-based CT quantification, visual analysis of invasive angiography, and quantitative coronary angiography.Insights into imaging · 2026Article
- Deep Learning-based Automated Coronary Plaque Quantification: First Demonstration With Ultra-high Resolution Photon-counting Detector CT at Different Temporal Resolutions.Investigative radiology · 2026Article
- Artificial intelligence across the cardiovascular diagnostic pathway: a case-based narrative review.Cardiovascular diagnosis and therapy · 2026Review
- Hemodynamic Significance of APEXCT Coronary Angiography Combined With Dynamic CT Myocardial Perfusion Imaging in Assessing Restenosis After Coronary Stenting.The Kaohsiung journal of medical sciences · 2026Article
- Construction and application of high-quality development model for public hospitals oriented to regional medical center: an integrated advanced imaging platform for computed tomography, magnetic resonance imaging, and positron emission tomography-computed tomography services.Journal of applied clinical medical physics · 2026Article
- Coronary Computed Tomography Angiography to Guide Percutaneous Coronary Intervention: Expert Opinion from a SCAI/SCCT Roundtable.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Article
- Review
- Combining artificial intelligence assisted image segmentation and ultrasound based radiomics for the prediction of carotid plaque stability.BMC medical imaging · 2025Article
- Computed Tomography Angiography in the Catheterization Laboratory: A Guide Towards Optimizing Coronary Interventions.Journal of cardiovascular development and disease · 2025Review
- Temporal changes in CT-derived fractional flow reserve in patients after heart transplantation.European radiology · 2025Article
- Assessing the need for coronary angiography in high-risk non-ST-elevation acute coronary syndrome patients using artificial intelligence and computed tomography.The international journal of cardiovascular imaging · 2025Article
- Revolutionizing Cardiac Imaging: A Scoping Review of Artificial Intelligence in Echocardiography, CTA, and Cardiac MRI.Journal of imaging · 2024Article
- Application of Quantitative Assessment of Coronary Atherosclerosis by Coronary Computed Tomographic Angiography.Korean journal of radiology · 2024Review
- Coronary angiography: a review of the state of the art and the evolution of angiography in cardio therapeutics.Frontiers in cardiovascular medicine · 2024Review
Corrections and comments
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Authors and funding
14 authors at 5 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose To evaluate the performance of a new artificial intelligence (AI)-based tool by comparing the quantified stenosis severity at coronary CT angiography (CCTA) with a reference standard derived from invasive quantitative coronary angiography (QCA). Materials and Methods This secondary, post hoc analysis included 120 participants (mean age, 59.7 years ± 10.8 [SD]; 73 [60.8%] men, 47 [39.2%] women) from three large clinical trials (AFFECTS, P3, REFINE) who underwent CCTA and invasive coronary angiography with QCA. Quantitative analysis of coronary stenosis severity at CCTA was performed using an AI-based coronary stenosis quantification (AI-CSQ) software service. Blinded comparison between QCA and AI-CSQ was measured on a per-vessel and per-patient basis. Results The per-vessel AI-CSQ diagnostic sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 80%, 88%, 86%, 65%, and 94%, respectively, for diameter stenosis (DS) 50% or greater; and 78%, 92%, 91%, 47%, and 98%, respectively, for DS 70% or greater. The areas under the receiver operating characteristic curve (AUCs) to predict DS of 50% or greater and 70% or greater on a per-vessel basis were 0.92 (95% CI: 0.88, 0.95;
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.